Abstract

In neuroimaging research, a wide variety of quantitative computational methods enable inference of results regarding the brain’s structure and function. In this chapter, we survey two broad families of approaches to quantitative analysis of neuroimaging data: statistical testing and machine learning. We discuss how methods developed for traditional scalar structural neuroimaging data have been extended to diffusion magnetic resonance imaging data. Diffusion MRI data have higher dimensionality and allow the study of the brain’s connection structure. The intended audience of this chapter includes students or researchers in neuroimage analysis who are interested in a high-level overview of methods for analyzing their data.

Bilder

Bibtex

@INCOLLECTION{ODonnell:TenDag2015,
author = {O'Donnell, Lauren and Schultz, Thomas},
pages = {299--319},
title = {Statistical and Machine Learning Methods for Neuroimaging: Examples, Challenges, and Extensions to
Diffusion Imaging Data},
booktitle = {Visualization and Processing of Higher Order Descriptors for Multi-Valued Data},
series = {Mathematics and Visualization},
year = {2015},
publisher = {Springer},
abstract = {In neuroimaging research, a wide variety of quantitative computational methods enable inference of
results regarding the brain’s structure and function. In this chapter, we survey two broad
families of approaches to quantitative analysis of neuroimaging data: statistical testing and
machine learning. We discuss how methods developed for traditional scalar structural neuroimaging
data have been extended to diffusion magnetic resonance imaging data. Diffusion MRI data have higher
dimensionality and allow the study of the brain’s connection structure. The intended audience of
this chapter includes students or researchers in neuroimage analysis who are interested in a
high-level overview of methods for analyzing their data.}
}